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The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system's sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS–users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems. [ABSTRACT FROM AUTHOR]

Integrated sensing and communication (ISAC) has been defined as one of the major usage scenarios for 6G. When sensing and communication channels coexist in ISAC scenarios, neither conventional communication nor sensing channel models are applicable. As the foundation of ISAC studies, a new channel modeling methodology is required to characterize both sensing and communication channels and their correlations. This article introduces the framework of a general ISAC channel model, which integrates a deterministic multiscattering-center (MSC) model of sensing targets to the stochastic propagation channel model. Parameterizations of the proposed channel model rely on channel measurements. This article introduces two ISAC channel measurement methodologies based on the vector network analyzer (VNA) and a novel dual-sensor measurement system. The proposed methods can be applied in future 6G ISAC channel model standardization and system evaluations. [ABSTRACT FROM AUTHOR]

The advent of 6G communication systems has enabled, through integrated sensing and communications (ISAC), the vision of the intelligent connection of everything. Since such ISAC systems will be utilizing the sub-6-GHz and millimeter-wave (mm-wave) bands, appropriate hybrid, i.e., sensing and communication channel models, are required for their proper operation. This article first presents a comprehensive survey of channel models used in ISAC applications viewed from different modeling perspectives by identifying the challenges encountered in these modeling approaches. A novel hybrid approach for ISAC channel modeling is then introduced wherein the modeling methodology is partitioned into three elements: targets, clutters, and interferences. The advantage of such a flexible modeling approach using these three elements enhances the proposed model’s ability to effectively deal with the specific sensing and communication operational requirements. Finally, further performance enhancement methods, based upon real-time 3D environment reconstruction techniques as well as artificial intelligence (AI), are proposed and discussed. [ABSTRACT FROM AUTHOR]

The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away fromunauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard. It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security, while maintaining a high-quality reconstruction of images at the receiver side. The proposed framework allows image watermarking based on the Discrete Cosine Transform (DCT) merged with the Singular Value Decomposition (SVD) in the so-called DCT-SVD watermarking. In addition, image encryption is implemented based on chaos and DNA encoding. The encrypted watermarked images are then transmitted through the wireless SC-FDMA system. The linearMinimumMean Square Error (MMSE) equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images. Two subcarrier mapping schemes, namely localized and interleaved schemes, are compared in this paper. The study depends on different channelmodels, namely PedestrianAandVehicularA, with amodulation technique namedQuadratureAmplitude Modulation (QAM). Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images. In addition, different variants of SC-FDMA based on the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Fast Fourier Transform (FFT) are considered and compared for the image communication task. The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels, while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS)-aided near-field communications are investigated. First, the necessity of investigating RIS-aided near-field communications and the advantages brought about by the unique spherical wave-based near-field propagation are discussed. Then, the family of patch array-based RISs and metasurface-based RISs is introduced along with respective near-field channel models. A pair of fundamental performance limits of RIS-aided near-field communications, namely, their power scaling law and effective degrees of freedom (EDOF), are analyzed for both patch array-based and metasurface-based RISs, which reveals the potential performance gains that can be achieved. Furthermore, associated near-field beam training and beamforming design issues are studied, where a two-stage hierarchical beam training approach and a low-complexity element-wise beamforming design are proposed for RIS-aided near-field communications. Finally, a suite of open research problems is highlighted for motivating future research. [ABSTRACT FROM AUTHOR]

Methods for calculating the impulse response of dispersive optical wireless channels have been known for a long time. However, a comprehensive procedure for predicting resulting bit error rates, starting from a highly accurate channel emulation method, has not yet been presented. To fill this gap, we propose a methodology for modeling realistic dispersive optical wireless channels that can be used directly for corresponding bit error rate simulations. The importance of proper power normalization of the multipath components is emphasized. Moreover, the optical channel model is embedded in an end-to-end electrical channel model suitable for intensity-modulation/direct detection schemes under linear operation. To significantly reduce the simulation time, a so-called chip-rate model is presented. Finally, based on the proposed modeling approach for dispersive channels, bit error rate simulations are performed for selected intensity modulation schemes with and without equalization at the receiver and are compared within a common and consistent framework.

This paper presents a simulation framework for modelling optical underwater turbulence in conjunction with absorption and scattering. Using this technique, the channel is evaluated in two ways: the turbulent statistics in different channel conditions; and the stationary channel characteristics. The turbulent statistics observed from the simulation show that the relative impact of turbulence on a received signal is lower in a highly scattering channel, showing an in-built resilience of these channels. Received intensity distributions are presented, showing that the commonly used Log-Normal fading model provides a good description of the fluctuations in received optical power due to the effect of turbulence. When considering stationary channel characteristics, the effect of turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. The impact of turbulence - as measured using this new modelling framework - on the channel capacity is equally investigated to provide context to the implications of the channel modelling findings on underwater optical wireless communications link performance.

Proximity estimation forms the backbone of contact tracing solutions, as quarantining potentially infected individuals is essential for controlling the spread of epidemics. It is also essential for device discovery in Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, which will be critical in future 6G networks. Despite the widespread coverage of cellular networks, no previous work has evaluated cellular-based proximity estimation in an experimental setting. In this paper, we collect Channel State Information (CSI) measurements from an actual cellular network and utilize them to train and evaluate two proposed solutions. Capitalizing on CSI spatial correlation, we propose a data-driven method to classify devices based on their respective proximity. The proposed neural network has an accuracy of 91.18% when classifying devices as within 5 meters from one another or not, from only 10 seconds of CSI measurements. This method is complemented by a model-based approach that provides a solid theoretical model for estimating proximity. The model-based approach uses Bayesian inference of the conditional distribution of power correlation across devices, assuming spatially-correlated shadowing and stochastic geometry tools. The proposed Bayesian regressor fits our dataset better than the standard exponentially-decaying correlation model, reaching a $2.8 \times $ lower RMSE while requiring fine-tuning of only two more parameters. A Bayesian classifier is also proposed and reached a 91.67% accuracy on the binary case while also outperforming the data-driven model significantly at higher distances.

The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system’s sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS–users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.

These Single carrier frequency division multiple access (SC-FDMA) has very low power consumption at the sender's side, and it is the access scheme used for the uplink in long-term evolution (LTE). The objective of this work is to explore the error probability of SC-FDMA system under sub-carrier mapping (SM) in heavily faded areas where the signal-to-noise ratios (SNRs) are very low. Wireless environment with heavily faded areas includes military radio systems; direct sequence spread spectrum system (DS-SS), global positioning system (GPS) etc. The localized FDMA (LFDMA) and distributed FDMA (DFDMA) are used to compare the performances of SC-FDMA in heavily faded areas. In heavily faded area with negative signalto- noise ratio (SNR), the SC-FDMA system is implemented using modulation and encoding methods to receive a very weak signal. Here, binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 16-PSK, quadrature amplitude modulation (QAM) and 16-QAM modulation techniques are used to calculate the bit error rate (BER) performances. The results show the BER performances of SC-FDMA using mapping schemes for different channels, like, AWGN channel, Rayleigh channel, COST207TU, and COST207RA channel models for heavily faded areas. In AWGN channel, BER at -15dB is about 10 times more than BER at 15dB. The COST207 model shows that the BER is less in typical urban (TU) area compared to the rural area (RA).The performance of BPSK modulation in SC-FDMA system is better in heavily faded areas than other modulation schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller’s rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data.

Deep learning utilization to optimize block-structured communication systems has attracted tremendous attention from researchers. Nevertheless, owing to the extensive data transmission between the transmitter and the receiver, communication, in this case, is hard to establish and maintain effectively. As a solution for this, we first investigate typical end-to-end learning for a communication system, Generative Adversarial Network (GAN). Then, two problems associated with GAN-based systems, the gradient vanishing and overfitting, are reviewed. Subsequently, a residual aided GAN (RA-GAN) is proposed as means to overcome these problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. Finally, the numerical results performed in MATLAB for simulation and Codelabs for training have proven that the RA-GAN scheme has near-optimal performance and outperforms the conventional GAN scheme. Throughout this case study, readers can understand the issues that would occur when deep learning is applied to a communication system and possible approaches to address them.

To explore the full potential of ultramassive multiple-input, multiple-output (MIMO) communication systems, it is fundamental to understand new ultramassive MIMO channel characteristics and establish pervasive channel models. On this basis, large-dimensional spatial-temporal transmission and random access technologies need to be investigated and evaluated for better practical implementation. First, this article reviews recent advances of ultramassive MIMO technologies in the traditional spatial domain, including wireless channel characterization and modeling, channel estimation, spatial multiplexing, and precoding. Second, considering the dramatic increase of base station (BS) antennas and access users in ultramassive MIMO systems, the confronted high-dimensional complexity and computing burden of these ultramassive MIMO technologies are indicated. To provide an efficient and systematic solution, the emerging tendency to transform related technologies from the traditional spatial domain to the beam domain is introduced. The utilities of large sparsity merit, reduced energy consumption, and improved usage of radio frequency (RF) chains in the beam domain channel are elaborated. Last, future challenges of ultramassive MIMO communication systems are discussed. [ABSTRACT FROM AUTHOR]

Due to low cost and low complexity, passive ultrahigh-frequency radio-frequency identification (UHF RFID) is gaining popularity in indoor localization. According to current RFID channel models, multipath propagation and noise are two significant factors that affect the accuracy of measured RF signals. To minimize the impacts of measurement errors on localization, scene analysis algorithms are proposed and got lots of attention in recent years. These algorithms make use of reference tags as similarity markers in neighboring locations to improve localization accuracy. However, densely deployed tags induce a new challenge, antenna interference, leading to the poor signal similarity between nearby tags. Nonetheless, there are no refined channel models for explaining the effect of spacing distances between tags on RF signal fluctuations, thus the choice of spacing distances becomes an outstanding issue for scene analysis localization algorithms. In this article, we propose Timing, the Tag Interference ModelING, for analyzing the relationship between signal fluctuations and spacing distances. Unlike inductive coupling in the near field, as the tags are all located at the far field of interacted antenna, we introduce microwave transmission line theory for channel model analysis. The experimental results verify that Timing is well suited to authentic situations. Additionally, we examine the performance of the ${k}$ -nearest neighbor (${k}$ -NN) algorithm under various spacing distances. The findings demonstrate that the spacing distance identified by Timing as a stable point achieves better accuracy with scene analysis algorithms. Therefore, Timing could be used as a critical foundation for the choice of spacing distances in future tag deployment applications. [ABSTRACT FROM AUTHOR]

We consider error-rate prediction for dual-use lighting systems with visible light communication (VLC) functionality. Since light planning rather than communications engineering is usually the driving discipline for practical dual-use settings, we extract VLC channel parameters as a by-product from advanced 3-dimensional (3D) light-planning models. By this means, we attain realistic signal-to-noise ratios for exemplary positions of a VLC receiver, which allows us to predict corresponding end-to-end bit error rates (BERs). Specifically, our procedure accounts for important aspects of state-of-art lighting systems, such as realistic light distribution curves of employed luminaires, possible presence unmodulated light sources, and, particularly, adaptive dimming operations in response to prevalent sunlight. In this paper, we (i) devise a methodology for VLC systems with optical receive filtering to convert photometric quantities into radiometric quantities, (ii) present examples of BER predictions for selected modulation schemes within some basic office environments, and (iii) conduct an analysis of the resulting simulation accuracy for a particular 3D light-planning tool. Simulation results show that our approach is indeed suited to predict realistic end-to-end BERs for dual-use lighting/VLC systems. Moreover, our procedure is fairly general and may be tailored to specific practical office settings and particular light plans of interest.

Wireless capsule endoscopy (WCE) is a modern, non-invasive method of gastrointestinal examination that can significantly reduce mortality and morbidity. One of the current challenges in WCE is the precise localization of the capsule. An accurate path loss propagation model can be used to find the exact distance from the surface to the capsule inside the abdominal cavity. Unfortunately, there are no standardized In-to-On-Body channel models describing the signal propagation at ultra-high frequencies that are used in the most commercially available WCE systems. This study addresses the gap by conducting an experimental validation of a new propagation model for WCE applications at 2.45 GHz. The results were confirmed by conducting two separate in-vivo trials on porcine animals under general anesthesia. The performance of the model as well as the corresponding ranging errors were evaluated when it was used as an inverse solution for distance estimation to an ingested transmitter. The main advantage of the model is its theoretical basis, which can help further generalize the findings for similar communication scenarios. The obtained ranging error was smaller than one centimeter, suggesting that it can be used for accurate range-based positioning of implanted transmitters.

In this study, an efficient model of the channel matrix is developed for a $2\times $ 2 wireless body area network multiple input output (WBAN-MIMO) system based on deep learning algorithms. The model is composed of three deep-learning algorithms. Moreover, the model simultaneously predicts channel matrix $H$ in an underground mine and identifies the position of the collected data in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The model was trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within a frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in the LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix ${H}$ with position and identifies whether the channel is a LoS or NLoS. Finally, the path loss and channel impulse response models were compared with measurement-based models. The modeled channel prediction exhibited a lower root mean square error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that deep learning WBAN-MIMO modeling offers a powerful solution for future wireless systems in underground mine environments.

We investigate propagation characteristics for wireless channels, applicable to Integrated Access and Backhaul (IAB) and relay-based networks with lamppost-height nodes at 5.5 GHz. We compare our empirical results with a variety of models that have been proposed for system simulation. Our work is based on an extensive measurement campaign in an urban environment, where we simultaneously measured base-relay, relay-mobile and base-mobile links. This simultaneity allows us to conclude that low-height relay nodes offer a minor path-loss advantage over the base-user link. Moreover, within the range of relay heights that we measured of 2.8 and 4.7 m, we observed no signi?cant gain associated with choosing the higher relay placement. Our results however also show that the base-relay link is quite stable over time and thus will lend itself to multi-antenna techniques requiring a small overhead in channel state information feedback. Our results add to the empirical data that the standards models are based on, providing path-loss results obtained simultaneously for all links of an urban relay-based system.

Due to the multifold growth in demands of multimedia services and mobile data, the request for increased channel capacity in mobile and wireless communication has been quickly increasing. Developing a wireless system with more spectral efficiency under varying channel condition is a key challenge to provide more bit rates with limited spectrum. Multiple Input Multiple Output (MIMO) system with Orthogonal Frequency Division Multiplexing (OFDM) gives higher gain by using the direct and the reflected signals, thus facilitating the transmission at high data rate. An integration of Spatial Modulation (SM) with OFDM (SM OFDM) is a newly evolved transmission technique and has been suggested as a replacement for MIMO -OFDM transmission. In practical scenarios, channel estimation is significant for detecting transmitted data coherently. This paper proposes pilot based, Minimum Mean Square Error (MMSE) channel estimation for the SM OFDM communication system. We have focused on analyzing Symbol Error Rate (SER) and Mean Square error (MSE) under Rayleigh channel employing International Telecommunication Union (ITU) specified Vehicular model of Pilot based MMSE channel estimator using windowed Discrete Fourier Transform (DFT) and MMSE weighting function. Simulation output shows that proposed estimator’s SER performance lies close to that of the MMSE optimal estimator in minimizing aliasing error and suppressing channel noise by using frequency domain data windowing and time domain weighting function. Usage of the Hanning window eliminates error floor and has a compact side lobe level compared to Hamming window and Rectangular window. Hanning window has a larger MSE at low Signal to Noise Ratio (SNR) values and decreases with high SNR values. It is concluded that data windowing technique can minimize the side lobe level and accordingly minimize channel estimation error when interpolation is done. MMSE weighting suppresses channel noise and improves estimation performance. Since Inverse Discrete Fourier Transform (IDFT)/DFT transforms can be implemented with fast algorithms Inverse Fast Fourier Transform( IFFT)/Fast Fourier Transform (FFT) computational complexity can be remarkably reduced.

In this article, a 3-D geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV’s altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements. [ABSTRACT FROM AUTHOR]

Non-orthogonal communications are expected to play a key role in future wireless systems. In downlink transmissions, the data symbols are broadcast from a base station to different users, which are superimposed with different power to facilitate high-integrity detection using successive interference cancellation (SIC). However, SIC requires accurate knowledge of both the channel model and channel state information (CSI), which may be difficult to acquire. We propose a deep learning-aided SIC detector termed SICNet, which replaces the interference cancellation blocks of SIC by deep neural networks (DNNs). Explicitly, SICNet jointly trains its internal DNN-aided blocks for inferring the soft information representing the interfering symbols in a data-driven fashion, rather than using hard-decision decoders as in classical SIC. As a result, SICNet reliably detects the superimposed symbols in the downlink of non-orthogonal systems without requiring any prior knowledge of the channel model, while being less sensitive to CSI uncertainty than its model-based counterpart. SICNet is also robust to changes in the number of users and to their power allocation. Furthermore, SICNet learns to produce accurate soft outputs, which facilitates improved soft-input error correction decoding compared to model-based SIC. Finally, we propose an online training method for SICNet under block fading, which exploits the channel decoding for accurately recovering online data labels for retraining, hence, allowing it to smoothly track the fading envelope without requiring dedicated pilots. Our numerical results show that SICNet approaches the performance of classical SIC under perfect CSI, while outperforming it under realistic CSI uncertainty. [ABSTRACT FROM AUTHOR]

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical $N$ -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user. [ABSTRACT FROM AUTHOR]

This paper investigates the secret key-authenticated-capacity region, where information-theoretic authentication is defined by the ability of the decoder to accept and decode messages originating from a valid encoder while rejecting messages from other invalid sources. The model considered here consists of a valid encoder-decoder pairing that can communicate through a channel controlled by an adversary who is also able to eavesdrop on the encoder’s transmissions. Prior to the encoder’s transmission, the adversary decides whether or not to replace the decoder’s observation with an arbitrary one of the adversary’s choosing, with the adversary’s objective being to have the decoder accept and decode their observation to a valid message (different from that of the encoder). To combat the adversary, the encoder and decoder share a secret key. The secret key-authenticated-capacity region is defined as the region of jointly achievable message rate, authentication rate (a to be defined per symbol measure that will generally represent the likelihood that an adversary can fool the decoder), and the key-consumption rate (how many bits of secret key are needed per symbol sent). This is the second of a two-part study, with the parts differing in their measure of the authentication rate. For this second study, the probability of false authentication is considered as a function of the system state, where the system state is defined by the message being transmitted, the value of the secret key, the adversary’s channel observations, and the adversary’s (possibly stochastic) choice for the decoder’s observation. Termed the typical-authentication rate, the authentication measure considered here corresponds to an upper bound on the probability of false authentication for the majority of system states. For this measure, we derive matching inner and outer bounds for the secret key-enabled authenticated capacity region in terms of traditional information-theoretic measures. In doing so, it is shown that the typical-authentication rate and the message rate exhibit a one-to-one trade-off in the capacity region. [ABSTRACT FROM AUTHOR]

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this letter, an unmanned aerial vehicle (UAV) three-dimensional (3D) deployment problem for millimeter wave (mmWave) communications is examined. Due to the sensitivity of mmWave signals to blockages, the existence of line-of-sight (LoS) links plays a vital role in guaranteeing high quality communication. To deploy UAVs effectively and ensure LoS communications, we propose a two-stage anti-block UAV deployment method based on building geometric analysis. First, by employing the established signal blockage aware mmWave channel model, a blockage testing K-means (BT-K-means) algorithm is proposed to implement user clustering and preliminary deployment of UAVs. Then, to overcome the difficulty of finding the optimal location of a UAV due to frequent local oscillation of the path loss (PL) function, a PL oscillation-aware UAV 3D coordinate search algorithm is proposed, which can avoid signal blockages and effectively reduce the total PL. Finally, we verify the effectiveness of the proposed method through our simulation results. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise. [ABSTRACT FROM AUTHOR]

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for $\mathrm {28~GHz}$ air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data. [ABSTRACT FROM AUTHOR]

In this article, a capacity-oriented envelope correlation coefficient (ECC) that incorporates a preferred channel model for assessing a multiple antenna system of a multiple-input-multiple-output (MIMO) mobile device is proposed. Originated from the rudimentary law of ergodic channel capacity, the ECC for multiple antennas (ECC-M) is proven to holistically reflect the impact of radiation patterns and total efficiencies of multiple antennas on channel capacity. The closed-form ECC-M expressions for Rayleigh fading channel model and ray-based spatial channel model are studied, leading to an easy-to-use ECC-M expression that approximately incorporates a realistic channel model. The correlation of the proposed ECC-M to the channel capacity is demonstrated through the Monte Carlo (MC) simulation of three four-element MIMO antenna testbeds in two channel models, showing that the antenna total efficiency and a realistic channel model involved in the ECC-M play an important role in assessing the performance of multiple antennas. It is justified that the difference in channel capacities of two multiple antenna systems is highly relevant to their ECC-Ms at high signal-to-noise ratios (SNRs). The proposed ECC-M is also experimentally validated through over-the-air (OTA) throughput measurement, revealing that the ECC-M can effectively assess the OTA performance of multiple antenna systems for MIMO terminal devices. [ABSTRACT FROM AUTHOR]

A cost-effective and accurate method for predicting over-the-air (OTA) data throughput of multiple-input and multiple-output (MIMO) wireless devices in the antenna design stage is proposed. The method incorporates passive characteristics of MIMO antennas, realistic channel model, and conductive measurement results in a computer simulation framework. With extracted relation between received power and signal-to-noise ratio (SNR) of a wireless device at antenna ports by a conductive test and measured passive characteristics of MIMO antennas, OTA throughput versus received power in a realistic channel model is obtained by the Monte Carlo simulation of Shannon’s law. The unique features of this method include: no channel emulator equipment is required; a wide variety of precoding schemes can be applied; passive characteristics of antennas are fully incorporated; and frequency-selective fading channel can be adopted. The method has been intensively validated by comparing the calculated OTA throughputs with those measured by a multiprobe anechoic chamber method under spatial channel model extension (SCME) in three aspects: spatial correlation, radiation efficiency, and mutual coupling, showing as low as 0.2–0.3 dB discrepancy in power level in most cases. The method can be used to evaluate a MIMO array antenna design of a wireless device with respect to OTA performance. [ABSTRACT FROM AUTHOR]

This paper investigates the secret-key-authenticated-capacity region, where information-theoretic authentication is defined by the ability of the decoder to accept and decode messages originating from a valid encoder while rejecting messages from other invalid sources. The model considered here consists of a valid encoder-decoder pairing that can communicate through a channel controlled by an adversary who is also able to eavesdrop on the encoder’s transmissions. Over multiple rounds of communication, the adversary first decides whether or not to replace the decoder’s observation with an arbitrary one of the adversary’s choosing, with the goal of the adversary being to have the decoder accept and decode their observation as a valid message (different from that of the encoder). To combat the adversary, the encoder and decoder share a secret key. The secret-key-authenticated-capacity region here is then defined as the region of jointly achievable message rate, authentication rate (a to be defined per symbol measure that will generally represent the likelihood that an adversary can fool the decoder), and the key-consumption rate (how many bits of secret key are needed per symbol sent). This is the first of a two-part study, with the parts differing in their measure of the authentication rate. In this first study, the authentication rate is the exponent of blocklength-normalized exponent of the expected probability of false authentication. For this metric, we provide an inner bound which improves on those existing in the literature. This is achieved by adopting and merging different classical techniques in novel ways. Within these classical secret-key-based authentication techniques, one technique derives authentication capability from secure channel coding to send the secret key with the message, and the other technique derives its authentication capability directly from obscuring the source. [ABSTRACT FROM AUTHOR]

We design efficient online scheduling policies to maximize the freshness of information delivered to the users in a cellular network under both adversarial and stochastic channel and mobility assumptions. The information freshness achieved by a policy is investigated through the lens of a recently proposed metric - Age-of-Information (AoI). We show that a natural greedy scheduling policy is competitive against any optimal offline policy in minimizing the AoI in the adversarial setting. We also derive universal lower bounds to the competitive ratio achievable by any online policy in the adversarial framework. In the stochastic setting, we show that a simple index policy is near-optimal for minimizing the average AoI in two different mobility scenarios. Further, we prove that the greedy scheduling policy minimizes the peak AoI for static users in the stochastic setting. Simulation results show that the proposed policies perform well under realistic conditions. [ABSTRACT FROM AUTHOR]

Streaming codes take a string of source symbols as input and output a string of coded symbols in real time, which eliminate the queueing delay of traditional block codes and are thus especially appealing for delay sensitive applications. Existing works on streaming code performance either focused on the asymptotic error-exponent analyses, or on the optimal code construction under deterministic adversarial channel models. In contrast, this work analyzes the exact error probability of random linear streaming codes (RLSCs) in the large field size regime over the stochastic i.i.d. symbol erasure channel model. A closed-form expression of the error probability of large-field-size RLSCs is derived under, simultaneously, the finite memory length and decoding deadline constraints. The result is then used to examine the intricate tradeoff between memory length (complexity), decoding deadline (delay), code rate (throughput), and error probability (reliability). Numerical evaluation shows that under the same code rate and error probability requirements, the end-to-end delay of RLSCs is 40–48% of that of the optimal block codes (i.e., MDS codes). This implies that switching from block codes to streaming codes not only eliminates the queueing delay completely (which accounts for the initial 50% of the delay reduction) but also improves the reliability (which accounts for the additional 2–10% delay reduction). [ABSTRACT FROM AUTHOR]

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the ergodic capacity of the Relay-Reconfigurable Intelligent Surface (Relay-RIS) system exploiting the three-dimensional channel model, which is different from existing works ignoring antenna height. Specifically, we propose a novel three-dimensional statistical channel for the Relay-RIS system. The upper bound expressions of the ergodic capacity are deduced in diverse propagation environments. Meanwhile, the impact of channel condition and deployment location on the capacity improvement is quantified, and the minimum number of reflecting elements to achieve a given rate threshold is obtained. Simulation results verify the effectiveness of the Relay-RIS system and the three-dimensional deployment strategy. [ABSTRACT FROM AUTHOR]

The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems. In this paper, a novel three dimensional (3D) space-time-frequency non-stationary geometry-based stochastic model (GBSM) is proposed for indoor VLC channels. The proposed VLC GBSM can capture unique indoor VLC channel characteristics such as the space-time-frequency non-stationarity caused by large light-emitting diode (LED) arrays in indoor scenarios, long travelling paths, and large bandwidths of visible light waves, respectively. In addition, the proposed model can support special radiation patterns of LEDs, 3D translational and rotational motions of the optical receiver (Rx), and can be applied to angle diversity receivers (ADRs). Key channel properties are simulated and analyzed, including the space-time-frequency correlation function (STFCF), received power, root mean square (RMS) delay spread, and path loss (PL). Simulation results verify the space-time-frequency non-stationarity in indoor VLC channels. In addition, the influence of light source radiation patterns, receiver rotations, and ADRs on channel characteristics have been investigated. Finally, the accuracy and practicality of the proposed model are validated by comparing the simulation result of channel 3dB bandwidth with the existing measurement data. The proposed channel model will play a supporting role in the design of future 6G VLC systems. [ABSTRACT FROM AUTHOR]

This work introduces a multiple-input multiple-output (MIMO) channel model to characterize propagation in a small satellite swarm environment. By using the Rician $K$ -factor, this model appropriately combines fixed line-of-sight (LoS), satellite-reflected, and earth-reflected (ER) channel components with a randomly varying diffuse component that accounts for satellite, earth, and atmospheric scattering. The proposed model is compared to ray tracing and used to elucidate the performance of (4, 4), (8, 8), and (16, 16) point-to-point MIMO systems. Indicatively, assuming 15 dBm of transmit power, a keep-out radius of $R_{KO} \geq 50$ m, and a 24 satellite swarm, (16, 16) MIMO systems achieve approximately 4–5 times the capacity of a (2, 2) polarization diversity-based system, a significant improvement for a swarm that operates as a fractionated system and autonomously makes decisions. It is also shown that a (16, 1) single-input multiple-output system has 130 times the range of a (2, 1) system, increasing proportionally the lifetime of the swarm as satellites drift apart. With an example power budget, we show that a 3U CubeSat with an orbit average power (OAP) of 12.9 W can support 16 MIMO antennas. This MIMO channel model is the first step in developing optimized communication systems for SmallSat swarms. [ABSTRACT FROM AUTHOR]

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability. [ABSTRACT FROM AUTHOR]

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds. [ABSTRACT FROM AUTHOR]

Resistive random-access memory (ReRAM) is an emerging non-volatile memory technology for high-density and high-speed data storage. However, the sneak path interference (SPI) occurred in the ReRAM crossbar array seriously affects its data recovery performance. In this letter, we first propose a quantized channel model of ReRAM, based on which we design both the one-bit and multi-bit channel quantizers by maximizing the mutual information of the channel. A key channel parameter that affects the quantizer design is the sneak path occurrence probability (SPOP) of the memory cell. We first use the average SPOP calculated statistically to design the quantizer, which leads to the same channel detector for different memory arrays. We then adopt the SPOP estimated separately for each memory array for the quantizer design, which is generated by an effective channel estimator and through an iterative detection and decoding scheme for the ReRAM channel. This results in an array-level SPI-aware adaptive detection and decoding approach. Moreover, since there is a strong correlation of the SPI that affects memory cells in the same rows/columns than that affecting cells in different rows/columns, we further derive a column-level scheme which outperforms the array-level scheme. We also propose a channel decomposition method that enables effective ways for theoretically analyzing the ReRAM channel. Simulation results show that the proposed SPI-aware adaptive detection and decoding schemes can approach the ideal performance with three quantization bits, with only one decoding iteration. [ABSTRACT FROM AUTHOR]

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS- assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel pre-estimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance. [ABSTRACT FROM AUTHOR]

Recently, reconfigurable intelligent surfaces (RISs) have been proposed as a novel solution to enhance wireless communications such as suppressing inter-cell interference. However, signals arriving at a conventional reflecting-type RIS can only be reflected towards one side, leading to a limited service coverage, especially in an indoor environment involving potential obstacles. In this paper, we consider an intelligent omni-surface (IOS) which can provide services for users on both sides by enabling simultaneous signal reflection and transmission. Specifically, we propose an IOS aided indoor communication system where an IOS is embedded in a wall between two independent access points (APs) to suppress inter-cell interference. Due to the independence of the APs, we design a distributed hybrid beamforming scheme consisting of digital beamforming at APs and IOS-based analog beamforming to maximize the sum rate without any exchange of channel state information (CSI) between APs. Simulation results indicate that the proposed system performs very close to an optimal centralized scheme, and has a better sum rate performance compared to existing schemes. [ABSTRACT FROM AUTHOR]

Compared with the conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting intelligent omini-surfaces (STAR-IOSs) are able to achieve 360° coverage “smart radio environments”. By splitting the energy or altering the active number of STAR-IOS elements, STAR-IOSs provide high flexibility of successive interference cancellation (SIC) orders for non-orthogonal multiple access (NOMA) systems. Based on the aforementioned advantages, this paper investigates a STAR-IOS-aided downlink NOMA network with randomly deployed users. We first propose three tractable channel models for different application scenarios, namely the central limit model, the curve fitting model, and the M-fold convolution model. More specifically, the central limit model fits the scenarios with large-size STAR-IOSs while the curve fitting model is extended to evaluate multi-cell networks. However, these two models cannot obtain accurate diversity orders. Hence, we figure out the M-fold convolution model to derive accurate diversity orders. We consider three protocols for STAR-IOSs, namely, the energy splitting (ES) protocol, the time switching (TS) protocol, and the mode switching (MS) protocol. Based on the ES protocol, we derive closed-form analytical expressions of outage probabilities for the paired NOMA users by the central limit model and the curve fitting model. Based on three STAR-IOS protocols, we derive the diversity gains of NOMA users by the M-fold convolution model. The analytical results reveal that the diversity gain of NOMA users is equal to the active number of STAR-IOS elements. Numerical results indicate that 1) in high signal-to-noise ratio regions, the central limit model performs as an upper bound of the simulation results, while a lower bound is obtained by the curve fitting model; 2) the TS protocol has the best performance but requesting more time blocks than other protocols; 3) the ES protocol outperforms the MS protocol as the ES protocol has higher diversity gains. [ABSTRACT FROM AUTHOR]

Our study proposes a UAV communications recovery strategy under meteorological conditions based on a ray tracing simulation of excessive path loss in four distinct three-dimensional (3D) urban environments. We start by reviewing the air-to-ground propagation loss model under meteorological conditions, as well as the specific attenuation of rain, fog, and snow, and we propose a new expression for line-of-sight (LoS) probability. Using the two frequency bands of 28 GHz and 71 GHz, we investigate the impact of specific attenuation caused by different weather conditions and analyze the relationship between the radius of the UAV coverage area and the elevation angle. Furthermore, we investigate the effects of the rainfall rate, liquid water density, and snowfall rate on the maximum coverage area and optimal height of the UAV. Eventually, we propose a strategy that involves compensating for the maximum path loss and adjusting the UAV’s position to recover the coverage of the UAV to ground users. Our results show that rain has the greatest impact on the UAV’s coverage area and optimum height among the three types of weather conditions. For various weather conditions, relative to Region 1, the percentage reduction in the maximum coverage radius of Region 2 to Region 4 increases gradually, and the extent of each increase is approximately 10%. Moreover, after adding the compensated path loss, the coverage radius of the UAV in the four regions is restored to a value slightly larger than that before the rain. In addition, rain caused the greatest reduction in UAV coverage for suburban environments and the lowest for high-rise urban environments.

Antenna-free channel models can reflect real multipath propagation and can be applied widely for performance analysis, simulation, and physical emulation, combined with specific antennas used in the communication systems. However, the millimeter-wave (mmWave) channel measurement is usually performed by steering horn antennas, and the measured channel responses are actually spatial convolution of the channel propagation models and antenna pattern, which is commonly referred to as the antenna embedding effect. In this work, we propose a novel antenna de-embedding algorithm based on the deconvolution with Tikhonov regularization. By suppressing parts of the observed responses which are disguised by noise, the Tikhonov regularization facilitates the deconvolution of antenna pattern and enables the extraction of propagation models. In particular, in order to minimize the impact of deconvolved noise, we design an optimization algorithm to obtain the appropriate regularization factor with low computational complexity. To validate the proposed approach, we have performed an indoor mmWave channel measurement campaign using two different steering horn antennas. The principal peaks in the synthesized channel responses are accurately reconstructed, and the signal-to-noise ratio (SNR) is improved. The experiments verify that the proposed scheme de-embeds effectively the antenna effect and leads to the antenna-free channel models. [ABSTRACT FROM AUTHOR]